Real-time estimation of airflow vector based on lidar observations for preview control

As part of control techniques, gust-alleviation systems using airborne Doppler lidar technology are expected to enhance aviation safety by significantly reducing the risk of turbulence-related accidents. Accurate measurement and estimation of the vertical wind velocity are very important in the successful implementation of such systems. An estimation algorithm for the airflow vector based on data from airborne lidars is proposed and investigated for preview control to prevent turbulence-induced aircraft accidents in flight. An existing technique - simple vector conversion - assumes that the wind field between the lidars is homogeneous, but this assumption fails when turbulence occurs due to a large wind-velocity fluctuation. The proposed algorithm stores the line-of-sight (LOS) wind data at every moment and uses recent and past LOS wind data to estimate the airflow vector and to extrapolate the wind field between the airborne twin lidars without the assumption of homogeneity. Two numerical experiments - using the ideal vortex model and numerical weather prediction, respectively - were conducted to evaluate the estimation performance of the proposed method. The proposed method has much better performance than simple vector conversion in both experiments, and it can estimate accurate two-dimensional wind-field distributions, unlike simple vector conversion. The estimation performance and the computational cost of the proposed method can satisfy the performance demand for preview control.